{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from res2net_origin import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_data_loader():    \n",
    "    transform_train = transforms.Compose([\n",
    "        transforms.RandomCrop(32, padding=4),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        RandAugment(),  # 使用 RandAugment\n",
    "        transforms.ToTensor(),\n",
    "        #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "        transforms.Normalize(\n",
    "            (0.5070751592371323, 0.48654887331495095, 0.4409178433670343),\n",
    "            (0.2673342858792401, 0.2564384629170883, 0.27615047132568404)\n",
    "        )\n",
    "    ])\n",
    "    \n",
    "    transform_test = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ])\n",
    "    \n",
    "\n",
    "    trainset = torchvision.datasets.CIFAR100(root='./data', train=True,\n",
    "                                             download=False, transform=transform_train)\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,\n",
    "                                              shuffle=True, num_workers=2)\n",
    "\n",
    "    testset = torchvision.datasets.CIFAR100(root='./data', train=False,\n",
    "                                            download=False, transform=transform_test)\n",
    "    testloader = torch.utils.data.DataLoader(testset, batch_size=128,\n",
    "                                             shuffle=False, num_workers=2)\n",
    "        # 实例化模型\n",
    "    return trainloader,testloader\n",
    "\n",
    "\n",
    "def plot_training_results(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs):\n",
    "    # Convert PyTorch tensors to NumPy arrays\n",
    "    train_losses = np.array(train_losses)\n",
    "    train_top1_accs = np.array(train_top1_accs)\n",
    "    train_top5_accs = np.array(train_top5_accs)\n",
    "    val_losses = np.array(val_losses)\n",
    "    val_top1_accs = np.array(val_top1_accs)\n",
    "    val_top5_accs = np.array(val_top5_accs)\n",
    "\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    \n",
    "    # 绘制损失曲线\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(train_losses, label='Training Loss')\n",
    "    plt.plot(val_losses, label='Validation Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.title('Training/Validation Loss Curve')\n",
    "    plt.legend()\n",
    "\n",
    "    # 绘制准确率曲线\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(train_top1_accs, label='Training Top-1 Accuracy')\n",
    "    plt.plot(val_top1_accs, label='Validation Top-1 Accuracy')\n",
    "    plt.plot(train_top5_accs, label='Training Top-5 Accuracy')\n",
    "    plt.plot(val_top5_accs, label='Validation Top-5 Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('Training/Validation Accuracy Curve')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# 训练模型\n",
    "def train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "       \n",
    "    train_losses = []\n",
    "    train_top1_accs = []\n",
    "    train_top5_accs = []\n",
    "    val_losses = []\n",
    "    val_top1_accs = []\n",
    "    val_top5_accs = []\n",
    "\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    " \n",
    "    for epoch in range(num_epochs):\n",
    "        print('第 {}/{} 轮训练'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # 每个epoch都有训练和验证阶段\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                model.train()  # 设置模型为训练模式\n",
    "            else:\n",
    "                model.eval()   # 设置模型为评估模式\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "            running_top1_corrects = 0\n",
    "            running_top5_corrects = 0\n",
    "\n",
    "            # 遍历数据\n",
    "            for inputs, labels in (trainloader if phase == 'train' else testloader):\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # 零化参数梯度\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # 前向传播\n",
    "                # 只在训练阶段追踪历史\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # 计算 top-1 和 top-5 准确率\n",
    "                    _, top5_preds = torch.topk(outputs, 5, dim=1)\n",
    "                    top1_correct = torch.sum(preds == labels.data)\n",
    "                    top5_correct = torch.sum(top5_preds == labels.view(-1, 1))\n",
    "\n",
    "                    # 只有在训练阶段反向传播+优化\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # 统计\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += top1_correct\n",
    "                running_top1_corrects += top1_correct\n",
    "                running_top5_corrects += top5_correct\n",
    "\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "\n",
    "            epoch_loss = running_loss / len(trainloader.dataset if phase == 'train' else testloader.dataset)\n",
    "            epoch_top1_acc = running_top1_corrects.double() / len(trainloader.dataset if phase == 'train' else testloader.dataset)\n",
    "            epoch_top5_acc = running_top5_corrects.double() / len(trainloader.dataset if phase == 'train' else testloader.dataset)\n",
    "\n",
    "            print('{} 损失: {:.4f} Top-1 准确率: {:.4f} Top-5 准确率: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_top1_acc, epoch_top5_acc))\n",
    "            \n",
    "            # 保存损失和准确率\n",
    "            if phase == 'train':\n",
    "                train_losses.append(epoch_loss)\n",
    "                train_top1_accs.append(epoch_top1_acc)\n",
    "                train_top5_accs.append(epoch_top5_acc)\n",
    "            else:\n",
    "                val_losses.append(epoch_loss)\n",
    "                val_top1_accs.append(epoch_top1_acc)\n",
    "                val_top5_accs.append(epoch_top5_acc)\n",
    "\n",
    "            # 深度复制模型\n",
    "            if phase == 'val' and epoch_top1_acc > best_acc:\n",
    "                best_acc = epoch_top1_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('训练完成，耗时 {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('最佳验证集准确率: {:4f}'.format(best_acc))\n",
    "\n",
    "    # 加载最佳模型权重\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    \n",
    "    # 绘制训练结果曲线\n",
    "\n",
    "    return model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 4.4255 Top-1 准确率: 0.0468 Top-5 准确率: 0.1650\n",
      "val 损失: 4.2816 Top-1 准确率: 0.0518 Top-5 准确率: 0.1909\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.9301 Top-1 准确率: 0.1032 Top-5 准确率: 0.2993\n",
      "val 损失: 4.0608 Top-1 准确率: 0.0833 Top-5 准确率: 0.2649\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 3.6897 Top-1 准确率: 0.1382 Top-5 准确率: 0.3690\n",
      "val 损失: 3.8950 Top-1 准确率: 0.1109 Top-5 准确率: 0.3206\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 3.5109 Top-1 准确率: 0.1670 Top-5 准确率: 0.4192\n",
      "val 损失: 3.7744 Top-1 准确率: 0.1231 Top-5 准确率: 0.3614\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 3.3729 Top-1 准确率: 0.1920 Top-5 准确率: 0.4564\n",
      "val 损失: 3.6641 Top-1 准确率: 0.1430 Top-5 准确率: 0.3958\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 3.2523 Top-1 准确率: 0.2117 Top-5 准确率: 0.4880\n",
      "val 损失: 3.5360 Top-1 准确率: 0.1634 Top-5 准确率: 0.4106\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 3.1453 Top-1 准确率: 0.2316 Top-5 准确率: 0.5152\n",
      "val 损失: 3.3841 Top-1 准确率: 0.1949 Top-5 准确率: 0.4560\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 3.0461 Top-1 准确率: 0.2481 Top-5 准确率: 0.5392\n",
      "val 损失: 3.2683 Top-1 准确率: 0.2135 Top-5 准确率: 0.4845\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 2.9361 Top-1 准确率: 0.2697 Top-5 准确率: 0.5682\n",
      "val 损失: 3.2902 Top-1 准确率: 0.2165 Top-5 准确率: 0.4870\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 2.8574 Top-1 准确率: 0.2826 Top-5 准确率: 0.5857\n",
      "val 损失: 3.0744 Top-1 准确率: 0.2500 Top-5 准确率: 0.5327\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 2.7746 Top-1 准确率: 0.2997 Top-5 准确率: 0.6039\n",
      "val 损失: 3.0190 Top-1 准确率: 0.2612 Top-5 准确率: 0.5438\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 2.7034 Top-1 准确率: 0.3136 Top-5 准确率: 0.6222\n",
      "val 损失: 3.0835 Top-1 准确率: 0.2629 Top-5 准确率: 0.5331\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 2.6233 Top-1 准确率: 0.3301 Top-5 准确率: 0.6396\n",
      "val 损失: 2.9181 Top-1 准确率: 0.2886 Top-5 准确率: 0.5707\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 2.5727 Top-1 准确率: 0.3390 Top-5 准确率: 0.6540\n",
      "val 损失: 3.0504 Top-1 准确率: 0.2548 Top-5 准确率: 0.5498\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 2.5055 Top-1 准确率: 0.3551 Top-5 准确率: 0.6654\n",
      "val 损失: 2.7788 Top-1 准确率: 0.3052 Top-5 准确率: 0.6049\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 2.4581 Top-1 准确率: 0.3613 Top-5 准确率: 0.6788\n",
      "val 损失: 2.7219 Top-1 准确率: 0.3128 Top-5 准确率: 0.6216\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3899 Top-1 准确率: 0.3763 Top-5 准确率: 0.6913\n",
      "val 损失: 2.6969 Top-1 准确率: 0.3275 Top-5 准确率: 0.6248\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3550 Top-1 准确率: 0.3875 Top-5 准确率: 0.6992\n",
      "val 损失: 3.0455 Top-1 准确率: 0.2779 Top-5 准确率: 0.5676\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3060 Top-1 准确率: 0.3954 Top-5 准确率: 0.7112\n",
      "val 损失: 2.6907 Top-1 准确率: 0.3273 Top-5 准确率: 0.6357\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2579 Top-1 准确率: 0.4049 Top-5 准确率: 0.7215\n",
      "val 损失: 2.7549 Top-1 准确率: 0.3195 Top-5 准确率: 0.6258\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2159 Top-1 准确率: 0.4147 Top-5 准确率: 0.7304\n",
      "val 损失: 2.5463 Top-1 准确率: 0.3499 Top-5 准确率: 0.6596\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1702 Top-1 准确率: 0.4249 Top-5 准确率: 0.7383\n",
      "val 损失: 2.5938 Top-1 准确率: 0.3528 Top-5 准确率: 0.6551\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1356 Top-1 准确率: 0.4326 Top-5 准确率: 0.7447\n",
      "val 损失: 2.5463 Top-1 准确率: 0.3565 Top-5 准确率: 0.6629\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0939 Top-1 准确率: 0.4386 Top-5 准确率: 0.7532\n",
      "val 损失: 2.5328 Top-1 准确率: 0.3652 Top-5 准确率: 0.6630\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0621 Top-1 准确率: 0.4469 Top-5 准确率: 0.7580\n",
      "val 损失: 2.4813 Top-1 准确率: 0.3744 Top-5 准确率: 0.6820\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0247 Top-1 准确率: 0.4581 Top-5 准确率: 0.7666\n",
      "val 损失: 2.5494 Top-1 准确率: 0.3652 Top-5 准确率: 0.6702\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9921 Top-1 准确率: 0.4612 Top-5 准确率: 0.7742\n",
      "val 损失: 2.4620 Top-1 准确率: 0.3871 Top-5 准确率: 0.6862\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9591 Top-1 准确率: 0.4704 Top-5 准确率: 0.7785\n",
      "val 损失: 2.4905 Top-1 准确率: 0.3741 Top-5 准确率: 0.6799\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9337 Top-1 准确率: 0.4767 Top-5 准确率: 0.7848\n",
      "val 损失: 2.4080 Top-1 准确率: 0.3861 Top-5 准确率: 0.6951\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9063 Top-1 准确率: 0.4828 Top-5 准确率: 0.7885\n",
      "val 损失: 2.5224 Top-1 准确率: 0.3740 Top-5 准确率: 0.6761\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8682 Top-1 准确率: 0.4909 Top-5 准确率: 0.7982\n",
      "val 损失: 2.4975 Top-1 准确率: 0.3834 Top-5 准确率: 0.6850\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8276 Top-1 准确率: 0.5010 Top-5 准确率: 0.8049\n",
      "val 损失: 2.4585 Top-1 准确率: 0.3874 Top-5 准确率: 0.6908\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8158 Top-1 准确率: 0.5048 Top-5 准确率: 0.8064\n",
      "val 损失: 2.4164 Top-1 准确率: 0.3927 Top-5 准确率: 0.7092\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7843 Top-1 准确率: 0.5099 Top-5 准确率: 0.8105\n",
      "val 损失: 2.4139 Top-1 准确率: 0.3977 Top-5 准确率: 0.7093\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7617 Top-1 准确率: 0.5149 Top-5 准确率: 0.8166\n",
      "val 损失: 2.5065 Top-1 准确率: 0.3807 Top-5 准确率: 0.6855\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7288 Top-1 准确率: 0.5232 Top-5 准确率: 0.8199\n",
      "val 损失: 2.2934 Top-1 准确率: 0.4131 Top-5 准确率: 0.7220\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7053 Top-1 准确率: 0.5300 Top-5 准确率: 0.8265\n",
      "val 损失: 2.3679 Top-1 准确率: 0.4048 Top-5 准确率: 0.7104\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6947 Top-1 准确率: 0.5305 Top-5 准确率: 0.8286\n",
      "val 损失: 2.2816 Top-1 准确率: 0.4268 Top-5 准确率: 0.7304\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6548 Top-1 准确率: 0.5392 Top-5 准确率: 0.8356\n",
      "val 损失: 2.1812 Top-1 准确率: 0.4350 Top-5 准确率: 0.7508\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6335 Top-1 准确率: 0.5461 Top-5 准确率: 0.8403\n",
      "val 损失: 2.2846 Top-1 准确率: 0.4278 Top-5 准确率: 0.7231\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6250 Top-1 准确率: 0.5473 Top-5 准确率: 0.8401\n",
      "val 损失: 2.3663 Top-1 准确率: 0.4099 Top-5 准确率: 0.7137\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5919 Top-1 准确率: 0.5569 Top-5 准确率: 0.8458\n",
      "val 损失: 2.3767 Top-1 准确率: 0.4089 Top-5 准确率: 0.7121\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5796 Top-1 准确率: 0.5592 Top-5 准确率: 0.8470\n",
      "val 损失: 2.2687 Top-1 准确率: 0.4264 Top-5 准确率: 0.7315\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5488 Top-1 准确率: 0.5663 Top-5 准确率: 0.8530\n",
      "val 损失: 2.2512 Top-1 准确率: 0.4345 Top-5 准确率: 0.7388\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5409 Top-1 准确率: 0.5688 Top-5 准确率: 0.8541\n",
      "val 损失: 2.2538 Top-1 准确率: 0.4377 Top-5 准确率: 0.7320\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5027 Top-1 准确率: 0.5798 Top-5 准确率: 0.8594\n",
      "val 损失: 2.3580 Top-1 准确率: 0.4240 Top-5 准确率: 0.7186\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4955 Top-1 准确率: 0.5790 Top-5 准确率: 0.8625\n",
      "val 损失: 2.2884 Top-1 准确率: 0.4367 Top-5 准确率: 0.7313\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4620 Top-1 准确率: 0.5866 Top-5 准确率: 0.8678\n",
      "val 损失: 2.3181 Top-1 准确率: 0.4217 Top-5 准确率: 0.7297\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4532 Top-1 准确率: 0.5907 Top-5 准确率: 0.8684\n",
      "val 损失: 2.2967 Top-1 准确率: 0.4340 Top-5 准确率: 0.7330\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4418 Top-1 准确率: 0.5940 Top-5 准确率: 0.8693\n",
      "val 损失: 2.2396 Top-1 准确率: 0.4393 Top-5 准确率: 0.7482\n",
      "\n",
      "训练完成，耗时 31m 17s\n",
      "最佳验证集准确率: 0.439300\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = res2net18().to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
